Analysis of Hepatic Fibrosis Risk Factors Using Artificial Neural Networks

Abstract

Introduction: Following excessive scarring, an accumulation of connective tissue in the liver causes fibrosis. This fibrosis is asymptomatic, but generates portal hypertension by deviation in intra-hepatic blood flow. When this destroys the hepatic architecture by inducing a dysfunction, it switches to cirrhosis. The factors involved are sometimes ill-defined. However, the most common are hepatitis B and C and alcohol abuse. The analysis of these factors is very complex. Methods: This study proposes an artificial intelligence tool, in particular artificial neural networks in data analysis. We consider risk factors as input variables to the system. We consider the risk of fibrosis as an output variable. Conclusion: When the learning of the network is carried out from the proper cases followed at our hospital service of Setif in Algeria, the transfer function created is adjusted to its minimum of errors. It then becomes possible to assign random values to the input of the system to read the risk of fibrosis at the output.